The candidate's objective is to build upon his background in clinical surgical oncology and human molecular genetics to become a leader in the translation of new genome technologies to the care of patients with breast cancer. Through the steps outlined current proposal the candidate will obtain necessary additional expertise in 1) genome science, 2) bioinformatics and statistics, and 3) clinical trial design to improve biomarker based prognostic models and validate them in prospective clinical trials. The candidate is well positioned in a committed group of senior investigators in genomics and bioinformatics who have made significant recent contributions in this field. His clinical expertise in breast oncology, his unique perspective on tissue acquisition for biomarker studies, and his unique access to correlative science development and clinical trials through the American College of Surgeons Oncology Group affords him the unique opportunity to bridge the gap between development of new knowledge and its validation in clinical trials. The candidate and his mentors have constructed a program of scientific development and investigation centered around testing the hypothesis that ascertainment of biologic variability in gene expression as well as incorporating protein biomarker expression to gene expression-based predictive models will improve the ability of these models to predict breast cancer outcomes, including metastasis to axillary lymph nodes and disease recurrence. Specifically, the aims are: Specific Aim 1: To measure variability in gene expression in TIN0M0 hormone receptor positive breast cancer in pre- and postmenopausal women and ascertain whether this variability changes expression-based model predictions of axillary metastasis and recurrence from breast cancer. Specific Aim 2: To determine whether enhanced pathologic staging of axillary lymph node status improves the accuracy of gene expression-based predictive models. Specific Aim 3: To determine whether incorporating protein biomarker data into a gene expression-based predictive tree model improves the predictive accuracy of the model Specific Aim 4: To prospectively test a gene expression and protein biomarker-based predictive tree model in a multi-institutional pilot study.